Accurately estimating the color appearance in a scene that has been photographed presents several challenges. There have generally been two approaches to estimating the color of a scene from a photograph of the scene. The first conventional approach is to understand the process of capturing the color well enough so that the colors in the scene can be estimated based on the colors captured in the photograph and knowledge of illumination source. For a digital camera this method would require knowledge of the camera's spectral sensitivity as well as any non-linearity associated with the camera. The second conventional approach is to place known colors in the scene and then create a transform from the captured colors to the known colors.
One modification of the first approach is to photograph a scene through more than the typical three filters. For example, an RGB camera (i.e. a camera that captures scenes in red, green, and blue) might photograph a scene through one or more filters so that six or more channels of data are generated. These multiple-channels of data can be used to estimate spectral values in the scene. Photographing the same locations in a scene with different filters with one exposure is a non-trivial task without specialized camera equipment. This is particularly true with respect to common smart-phone cameras which would require a complex optical element to photograph the same locations in a scene with a single exposure and different filters. Attempting to resolve the problem by using multiple exposures has the disadvantage of allowing possible changes in lighting and camera position to degrade accuracy of the estimation of spectral values in the scene. Further, in camera processing might introduce more inaccuracies.